Event sequence analysis

ABSTRACT

Embodiments are directed to managing event information. A plurality of events associated with entities may be provided. A plurality of state types may be determined based on the plurality of events such that each state type is associated with a state. State keys associated with each entity may be determined based on events associated with each entity and the state types. A state stream for each entity may be provided based on their state keys such that each state stream may be an ordered sequence of the keys associated with each entity. In response to a query that includes a pattern filter, the pattern filter may be employed to determine a portion of the entities based on the state stream for each entity such that the pattern filter matches the state stream for each of the portion of the entities.

TECHNICAL FIELD

The present invention relates generally to data analysis, and moreparticularly, but not exclusively to, event sequence analysis.

BACKGROUND

Organizations are generating and collecting an ever increasing amount ofdata. This data may be associated with disparate parts of theorganization, such as, consumer activity, manufacturing activity,customer service, server logs, or the like. Further, in many cases, thedata represents a change in state associated with a given activity. Insome cases, modern data processing systems may enable organizations toview the current state of a system. However, often informationassociated with a sequence of actions or events that result in a currentstate may be difficult to discern using conventional data processingsystems. Even if detailed logs or event histories may be maintained orcollected, the construction or execution of queries, or the like, thatprovide actionable insights may be limited because of various factors,including, the amount of data, the intermingling of data, the lack ofstructure, or the like. Thus, it is with respect to these considerationsand others that the present invention has been made.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present innovationsare described with reference to the following drawings. In the drawings,like reference numerals refer to like parts throughout the variousfigures unless otherwise specified. For a better understanding of thedescribed innovations, reference will be made to the following DetailedDescription of Various Embodiments, which is to be read in associationwith the accompanying drawings, wherein:

FIG. 1 illustrates a system environment in which various embodiments maybe implemented;

FIG. 2 illustrates a schematic embodiment of a client computer;

FIG. 3 illustrates a schematic embodiment of a network computer;

FIG. 4 illustrates a logical architecture of a system for event sequenceanalysis in accordance with one or more of the various embodiments;

FIG. 5 illustrates a logical schematic of data structures for eventsequence analysis in accordance with one or more of the variousembodiments;

FIG. 6 illustrates a logical representation of a portion of a userinterface for event sequence analysis in accordance with one or more ofthe various embodiments;

FIG. 7 illustrates an overview flowchart of a process for event sequenceanalysis in accordance with one or more of the various embodiments;

FIG. 8 illustrates a flowchart of a process for event sequence analysisin accordance with one or more of the various embodiments; and

FIG. 9 illustrates a flowchart of a process for event sequence analysisin accordance with one or more of the various embodiments.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific exemplary embodiments bywhich the invention may be practiced. The embodiments may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the embodiments to those skilled in the art.Among other things, the various embodiments may be methods, systems,media or devices. Accordingly, the various embodiments may take the formof an entirely hardware embodiment, an entirely software embodiment oran embodiment combining software and hardware aspects. The followingdetailed description is, therefore, not to be taken in a limiting sense.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrase “in one embodiment” as used herein doesnot necessarily refer to the same embodiment, though it may.Furthermore, the phrase “in another embodiment” as used herein does notnecessarily refer to a different embodiment, although it may. Thus, asdescribed below, various embodiments may be readily combined, withoutdeparting from the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or”operator, and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The term “based on” is not exclusive andallows for being based on additional factors not described, unless thecontext clearly dictates otherwise. In addition, throughout thespecification, the meaning of “a,” “an,” and “the” include pluralreferences. The meaning of “in” includes “in” and “on.”

For example embodiments, the following terms are also used hereinaccording to the corresponding meaning, unless the context clearlydictates otherwise.

As used herein the term, “engine” refers to logic embodied in hardwareor software instructions, which can be written in a programminglanguage, such as C, C++, Objective-C, COBOL, Java™, PHP, Perl,JavaScript, Ruby, VBScript, Microsoft .NET™ languages such as C#, or thelike. An engine may be compiled into executable programs or written ininterpreted programming languages. Software engines may be callable fromother engines or from themselves. Engines described herein refer to oneor more logical modules that can be merged with other engines orapplications, or can be divided into sub-engines. The engines can bestored in non-transitory computer-readable medium or computer storagedevice and be stored on and executed by one or more general purposecomputers, thus creating a special purpose computer configured toprovide the engine.

As used herein, the term “data source” refers to databases,applications, services, file systems, or the like, that store or provideinformation for an organization. Examples of data sources may include,RDBMS databases, graph databases, spreadsheets, file systems, documentmanagement systems, local or remote data streams, or the like. In somecases, data sources are organized around one or more tables ortable-like structure. In other cases, data sources be organized as agraph or graph-like structure.

As used herein the term “configuration information” refers toinformation that may include rule based policies, pattern matching,scripts (e.g., computer readable instructions), or the like, that may beprovided from various sources, including, configuration files,databases, user input, built-in defaults, or the like, or combinationthereof.

The following briefly describes embodiments of the invention in order toprovide a basic understanding of some aspects of the invention. Thisbrief description is not intended as an extensive overview. It is notintended to identify key or critical elements, or to delineate orotherwise narrow the scope. Its purpose is merely to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Briefly stated, various embodiments are directed to managing eventinformation using one or more processors that execute one or moreinstructions to perform as described herein.

In one or more of the various embodiments, a plurality of events thatare associated with one or more entities may be provided.

In one or more of the various embodiments, a plurality of state typesmay be determined based on the plurality of events such that each statetype is associated with a state key and such that each state key may bedistinct from other state keys. In some embodiments, determining thestate types may include, determining one or more fields in the pluralityof events that may be associated with one or more state transitionsassociated with the one or more entities that may be associated with theplurality of events.

In one or more of the various embodiments, one or more state keys thatare associated with each entity may be determined based on one or moreevents associated with each entity and the one or more state types.

In one or more of the various embodiments, a state stream for eachentity may be provided based on their one or more state keys such thateach state stream may be an ordered sequence of the one or more statekeys that may be associated with each entity.

In one or more of the various embodiments, in response to a query thatincludes a pattern filter, further actions may be performed, including:employing the pattern filter to determine a portion of the one or moreentities based on the state stream for each entity such that the patternfilter matches the state stream for each of the portion of the entities;generating a query result based on the query and the portion of the oneor more entities; or the like. In some embodiments, the pattern filtermay include a regular expression that may be arranged to match one ormore sequences of state keys.

In one or more of the various embodiments, determining the portion ofthe one or more entities may include: comparing each sequence of statekeys to the pattern filter; determining one or more state streams basedon an affirmative result of the comparison; employing the one or moredetermined state streams to determine each entity that corresponds tothe one or more determined state streams; or the like.

In one or more of the various embodiments, the one or more state keysthat are associated with each entity may be concatenated into one ormore sequences of states.

Illustrated Operating Environment

FIG. 1 shows components of one embodiment of an environment in whichembodiments of the invention may be practiced. Not all of the componentsmay be required to practice the invention, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the invention. As shown, system 100 of FIG.1 includes local area networks (LANs)/wide area networks(WANs)-(network) 110, wireless network 108, client computers 102-105,event analysis server computer 116, application server computer 118, orthe like.

At least one embodiment of client computers 102-105 is described in moredetail below in conjunction with FIG. 2. In one embodiment, at leastsome of client computers 102-105 may operate over one or more wired orwireless networks, such as networks 108, or 110. Generally, clientcomputers 102-105 may include virtually any computer capable ofcommunicating over a network to send and receive information, performvarious online activities, offline actions, or the like. In oneembodiment, one or more of client computers 102-105 may be configured tooperate within a business or other entity to perform a variety ofservices for the business or other entity. For example, client computers102-105 may be configured to operate as a web server, firewall, clientapplication, media player, mobile telephone, game console, desktopcomputer, or the like. However, client computers 102-105 are notconstrained to these services and may also be employed, for example, asfor end-user computing in other embodiments. It should be recognizedthat more or less client computers (as shown in FIG. 1) may be includedwithin a system such as described herein, and embodiments are thereforenot constrained by the number or type of client computers employed.

Computers that may operate as client computer 102 may include computersthat typically connect using a wired or wireless communications mediumsuch as personal computers, multiprocessor systems, microprocessor-basedor programmable electronic devices, network PCs, or the like. In someembodiments, client computers 102-105 may include virtually any portablecomputer capable of connecting to another computer and receivinginformation such as, laptop computer 103, mobile computer 104, tabletcomputers 105, or the like. However, portable computers are not solimited and may also include other portable computers such as cellulartelephones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers,wearable computers, integrated devices combining one or more of thepreceding computers, or the like. As such, client computers 102-105typically range widely in terms of capabilities and features. Moreover,client computers 102-105 may access various computing applications,including a browser, or other web-based application.

A web-enabled client computer may include a browser application that isconfigured to send requests and receive responses over the web. Thebrowser application may be configured to receive and display graphics,text, multimedia, and the like, employing virtually any web-basedlanguage. In one embodiment, the browser application is enabled toemploy JavaScript, HyperText Markup Language (HTML), eXtensible MarkupLanguage (XML), JavaScript Object Notation (JSON), Cascading StyleSheets (CSS), or the like, or combination thereof, to display and send amessage. In one embodiment, a user of the client computer may employ thebrowser application to perform various activities over a network(online). However, another application may also be used to performvarious online activities.

Client computers 102-105 also may include at least one other clientapplication that is configured to receive or send content betweenanother computer. The client application may include a capability tosend or receive content, or the like. The client application may furtherprovide information that identifies itself, including a type,capability, name, and the like. In one embodiment, client computers102-105 may uniquely identify themselves through any of a variety ofmechanisms, including an Internet Protocol (IP) address, a phone number,Mobile Identification Number (MIN), an electronic serial number (ESN), aclient certificate, or other device identifier. Such information may beprovided in one or more network packets, or the like, sent between otherclient computers, event analysis server computer 116, application servercomputer 118, or other computers.

Client computers 102-105 may further be configured to include a clientapplication that enables an end-user to log into an end-user accountthat may be managed by another computer, such as event analysis servercomputer 116, application server computer 118, or the like. Such anend-user account, in one non-limiting example, may be configured toenable the end-user to manage one or more online activities, includingin one non-limiting example, project management, software development,system administration, configuration management, search activities,social networking activities, browse various websites, communicate withother users, or the like. Also, client computers may be arranged toenable users to display reports, interactive user-interfaces, or resultsprovided by event analysis server computer 116, application servercomputer 118, or the like.

Wireless network 108 is configured to couple client computers 103-105and its components with network 110. Wireless network 108 may includeany of a variety of wireless sub-networks that may further overlaystand-alone ad-hoc networks, and the like, to provide aninfrastructure-oriented connection for client computers 103-105. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. In one embodiment, the system mayinclude more than one wireless network.

Wireless network 108 may further include an autonomous system ofterminals, gateways, routers, and the like connected by wireless radiolinks, and the like. These connectors may be configured to move freelyand randomly and organize themselves arbitrarily, such that the topologyof wireless network 108 may change rapidly.

Wireless network 108 may further employ a plurality of accesstechnologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, andthe like. Access technologies such as 2G, 3G, 4G, 5G, and future accessnetworks may enable wide area coverage for mobile computers, such asclient computers 103-105 with various degrees of mobility. In onenon-limiting example, wireless network 108 may enable a radio connectionthrough a radio network access such as Global System for Mobilecommunication (GSM), General Packet Radio Services (GPRS), Enhanced DataGSM Environment (EDGE), code division multiple access (CDMA), timedivision multiple access (TDMA), Wideband Code Division Multiple Access(WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution(LTE), and the like. In essence, wireless network 108 may includevirtually any wireless communication mechanism by which information maytravel between client computers 103-105 and another computer, network, acloud-based network, a cloud instance, or the like.

Network 110 is configured to couple network computers with othercomputers, including, event analysis server computer 116, applicationserver computer 118, client computers 102, and client computers 103-105through wireless network 108, or the like. Network 110 is enabled toemploy any form of computer readable media for communicating informationfrom one electronic device to another. Also, network 110 can include theInternet in addition to local area networks (LANs), wide area networks(WANs), direct connections, such as through a universal serial bus (USB)port, Ethernet port, other forms of computer-readable media, or anycombination thereof. On an interconnected set of LANs, including thosebased on differing architectures and protocols, a router acts as a linkbetween LANs, enabling messages to be sent from one to another. Inaddition, communication links within LANs typically include twisted wirepair or coaxial cable, while communication links between networks mayutilize analog telephone lines, full or fractional dedicated digitallines including T1, T2, T3, and T4, or other carrier mechanismsincluding, for example, E-carriers, Integrated Services Digital Networks(ISDNs), Digital Subscriber Lines (DSLs), wireless links includingsatellite links, or other communications links known to those skilled inthe art. Moreover, communication links may further employ any of avariety of digital signaling technologies, including without limit, forexample, DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like.Furthermore, remote computers and other related electronic devices couldbe remotely connected to either LANs or WANs via a modem and temporarytelephone link. In one embodiment, network 110 may be configured totransport information of an Internet Protocol (IP).

Additionally, communication media typically embodies computer readableinstructions, data structures, program modules, or other transportmechanism and includes any information non-transitory delivery media ortransitory delivery media. By way of example, communication mediaincludes wired media such as twisted pair, coaxial cable, fiber optics,wave guides, and other wired media and wireless media such as acoustic,RF, infrared, and other wireless media.

Also, one embodiment of event analysis server computer 116 is describedin more detail below in conjunction with FIG. 3. Although FIG. 1illustrates event analysis server computer 116 or the like, as a singlecomputer, the innovations or embodiments are not so limited. Forexample, one or more functions of event analysis server computer 116, orthe like, may be distributed across one or more distinct networkcomputers. Moreover, in one or more embodiments, event analysis servercomputer 116 may be implemented using a plurality of network computers.Further, in one or more of the various embodiments, event analysisserver computer 116, or the like, may be implemented using one or morecloud instances in one or more cloud networks. Accordingly, theseinnovations and embodiments are not to be construed as being limited toa single environment, and other configurations, and other architecturesare also envisaged.

Illustrative Client Computer

FIG. 2 shows one embodiment of client computer 200 that may include manymore or less components than those shown. Client computer 200 mayrepresent, for example, one or more embodiment of mobile computers orclient computers shown in FIG. 1.

Client computer 200 may include processor 202 in communication withmemory 204 via bus 228. Client computer 200 may also include powersupply 230, network interface 232, audio interface 256, display 250,keypad 252, illuminator 254, video interface 242, input/output interface238, haptic interface 264, global positioning systems (GPS) receiver258, open air gesture interface 260, temperature interface 262,camera(s) 240, projector 246, pointing device interface 266,processor-readable stationary storage device 234, and processor-readableremovable storage device 236. Client computer 200 may optionallycommunicate with a base station (not shown), or directly with anothercomputer. And in one embodiment, although not shown, a gyroscope may beemployed within client computer 200 to measuring or maintaining anorientation of client computer 200.

Power supply 230 may provide power to client computer 200. Arechargeable or non-rechargeable battery may be used to provide power.The power may also be provided by an external power source, such as anAC adapter or a powered docking cradle that supplements or recharges thebattery.

Network interface 232 includes circuitry for coupling client computer200 to one or more networks, and is constructed for use with one or morecommunication protocols and technologies including, but not limited to,protocols and technologies that implement any portion of the OSI modelfor mobile communication (GSM), CDMA, time division multiple access(TDMA), UDP, TCP/IP, SMS, MMS, GPRS, WAP, UWB, WiMax, SIP/RTP, GPRS,EDGE, WCDMA, LTE, UMTS, OFDM, CDMA2000, EV-DO, HSDPA, or any of avariety of other wireless communication protocols. Network interface 232is sometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 256 may be arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 256 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. A microphone in audio interface 256 can also be used forinput to or control of client computer 200, e.g., using voicerecognition, detecting touch based on sound, and the like.

Display 250 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. Display 250 may also include a touch interface 244arranged to receive input from an object such as a stylus or a digitfrom a human hand, and may use resistive, capacitive, surface acousticwave (SAW), infrared, radar, or other technologies to sense touch orgestures.

Projector 246 may be a remote handheld projector or an integratedprojector that is capable of projecting an image on a remote wall or anyother reflective object such as a remote screen.

Video interface 242 may be arranged to capture video images, such as astill photo, a video segment, an infrared video, or the like. Forexample, video interface 242 may be coupled to a digital video camera, aweb-camera, or the like. Video interface 242 may comprise a lens, animage sensor, and other electronics. Image sensors may include acomplementary metal-oxide-semiconductor (CMOS) integrated circuit,charge-coupled device (CCD), or any other integrated circuit for sensinglight.

Keypad 252 may comprise any input device arranged to receive input froma user. For example, keypad 252 may include a push button numeric dial,or a keyboard. Keypad 252 may also include command buttons that areassociated with selecting and sending images.

Illuminator 254 may provide a status indication or provide light.Illuminator 254 may remain active for specific periods of time or inresponse to event messages. For example, when illuminator 254 is active,it may back-light the buttons on keypad 252 and stay on while the clientcomputer is powered. Also, illuminator 254 may back-light these buttonsin various patterns when particular actions are performed, such asdialing another client computer. Illuminator 254 may also cause lightsources positioned within a transparent or translucent case of theclient computer to illuminate in response to actions.

Further, client computer 200 may also comprise hardware security module(HSM) 268 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information such as,keys, digital certificates, passwords, passphrases, two-factorauthentication information, or the like. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI), and may be employed to generate, manage, orstore keys pairs, or the like. In some embodiments, HSM 268 may be astand-alone computer, in other cases, HSM 268 may be arranged as ahardware card that may be added to a client computer.

Client computer 200 may also comprise input/output interface 238 forcommunicating with external peripheral devices or other computers suchas other client computers and network computers. The peripheral devicesmay include an audio headset, virtual reality headsets, display screenglasses, remote speaker system, remote speaker and microphone system,and the like. Input/output interface 238 can utilize one or moretechnologies, such as Universal Serial Bus (USB), Infrared, WiFi, WiMax,Bluetooth™, and the like.

Input/output interface 238 may also include one or more sensors fordetermining geolocation information (e.g., GPS), monitoring electricalpower conditions (e.g., voltage sensors, current sensors, frequencysensors, and so on), monitoring weather (e.g., thermostats, barometers,anemometers, humidity detectors, precipitation scales, or the like), orthe like. Sensors may be one or more hardware sensors that collect ormeasure data that is external to client computer 200.

Haptic interface 264 may be arranged to provide tactile feedback to auser of the client computer. For example, the haptic interface 264 maybe employed to vibrate client computer 200 in a particular way whenanother user of a computer is calling. Temperature interface 262 may beused to provide a temperature measurement input or a temperaturechanging output to a user of client computer 200. Open air gestureinterface 260 may sense physical gestures of a user of client computer200, for example, by using single or stereo video cameras, radar, agyroscopic sensor inside a computer held or worn by the user, or thelike. Camera 240 may be used to track physical eye movements of a userof client computer 200.

GPS transceiver 258 can determine the physical coordinates of clientcomputer 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 258 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of client computer 200 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 258 can determine a physical location for clientcomputer 200. In one or more embodiments, however, client computer 200may, through other components, provide other information that may beemployed to determine a physical location of the client computer,including for example, a Media Access Control (MAC) address, IP address,and the like.

In at least one of the various embodiments, applications, such as,operating system 206, other client apps 224, web browser 226, or thelike, may be arranged to employ geo-location information to select oneor more localization features, such as, time zones, languages,currencies, calendar formatting, or the like. Localization features maybe used in display objects, data models, data objects, user-interfaces,reports, as well as internal processes or databases. In at least one ofthe various embodiments, geo-location information used for selectinglocalization information may be provided by GPS 258. Also, in someembodiments, geolocation information may include information providedusing one or more geolocation protocols over the networks, such as,wireless network 108 or network 111.

Human interface components can be peripheral devices that are physicallyseparate from client computer 200, allowing for remote input or outputto client computer 200. For example, information routed as describedhere through human interface components such as display 250 or keyboard252 can instead be routed through network interface 232 to appropriatehuman interface components located remotely. Examples of human interfaceperipheral components that may be remote include, but are not limitedto, audio devices, pointing devices, keypads, displays, cameras,projectors, and the like. These peripheral components may communicateover a Pico Network such as Bluetooth™, Zigbee™ and the like. Onenon-limiting example of a client computer with such peripheral humaninterface components is a wearable computer, which might include aremote pico projector along with one or more cameras that remotelycommunicate with a separately located client computer to sense a user'sgestures toward portions of an image projected by the pico projectoronto a reflected surface such as a wall or the user's hand.

A client computer may include web browser application 226 that isconfigured to receive and to send web pages, web-based messages,graphics, text, multimedia, and the like. The client computer's browserapplication may employ virtually any programming language, including awireless application protocol messages (WAP), and the like. In one ormore embodiments, the browser application is enabled to employ HandheldDevice Markup Language (HDML), Wireless Markup Language (WML),WMLScript, JavaScript, Standard Generalized Markup Language (SGML),HyperText Markup Language (HTML), eXtensible Markup Language (XML),HTML5, and the like.

Memory 204 may include RAM, ROM, or other types of memory. Memory 204illustrates an example of computer-readable storage media (devices) forstorage of information such as computer-readable instructions, datastructures, program modules or other data. Memory 204 may store BIOS 208for controlling low-level operation of client computer 200. The memorymay also store operating system 206 for controlling the operation ofclient computer 200. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX, orLINUX™, or a specialized client computer communication operating systemsuch as Windows Phone™, or the Symbian® operating system. The operatingsystem may include, or interface with a Java virtual machine module thatenables control of hardware components or operating system operationsvia Java application programs.

Memory 204 may further include one or more data storage 210, which canbe utilized by client computer 200 to store, among other things,applications 220 or other data. For example, data storage 210 may alsobe employed to store information that describes various capabilities ofclient computer 200. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 210 may also be employed to store socialnetworking information including address books, buddy lists, aliases,user profile information, or the like. Data storage 210 may furtherinclude program code, data, algorithms, and the like, for use by aprocessor, such as processor 202 to execute and perform actions. In oneembodiment, at least some of data storage 210 might also be stored onanother component of client computer 200, including, but not limited to,non-transitory processor-readable removable storage device 236,processor-readable stationary storage device 234, or even external tothe client computer.

Applications 220 may include computer executable instructions which,when executed by client computer 200, transmit, receive, or otherwiseprocess instructions and data. Applications 220 may include, forexample, other client applications 224, web browser 226, or the like.Client computers may be arranged to exchange communications one or moreservers.

Other examples of application programs include calendars, searchprograms, email client applications, IM applications, SMS applications,Voice Over Internet Protocol (VOIP) applications, contact managers, taskmanagers, transcoders, database programs, word processing programs,security applications, spreadsheet programs, games, search programs,visualization applications, and so forth.

Additionally, in one or more embodiments (not shown in the figures),client computer 200 may include an embedded logic hardware deviceinstead of a CPU, such as, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic(PAL), or the like, or combination thereof. The embedded logic hardwaredevice may directly execute its embedded logic to perform actions. Also,in one or more embodiments (not shown in the figures), client computer200 may include one or more hardware micro-controllers instead of CPUs.In one or more embodiments, the one or more micro-controllers maydirectly execute their own embedded logic to perform actions and accessits own internal memory and its own external Input and Output Interfaces(e.g., hardware pins or wireless transceivers) to perform actions, suchas System On a Chip (SOC), or the like.

Illustrative Network Computer

FIG. 3 shows one embodiment of network computer 300 that may be includedin a system implementing one or more of the various embodiments. Networkcomputer 300 may include many more or less components than those shownin FIG. 3. However, the components shown are sufficient to disclose anillustrative embodiment for practicing these innovations. Networkcomputer 300 may represent, for example, one embodiment of at least oneof event analysis server computer 116, or the like, of FIG. 1.

Network computers, such as, network computer 300 may include a processor302 that may be in communication with a memory 304 via a bus 328. Insome embodiments, processor 302 may be comprised of one or more hardwareprocessors, or one or more processor cores. In some cases, one or moreof the one or more processors may be specialized processors designed toperform one or more specialized actions, such as, those describedherein. Network computer 300 also includes a power supply 330, networkinterface 332, audio interface 356, display 350, keyboard 352,input/output interface 338, processor-readable stationary storage device334, and processor-readable removable storage device 336. Power supply330 provides power to network computer 300.

Network interface 332 includes circuitry for coupling network computer300 to one or more networks, and is constructed for use with one or morecommunication protocols and technologies including, but not limited to,protocols and technologies that implement any portion of the OpenSystems Interconnection model (OSI model), global system for mobilecommunication (GSM), code division multiple access (CDMA), time divisionmultiple access (TDMA), user datagram protocol (UDP), transmissioncontrol protocol/Internet protocol (TCP/IP), Short Message Service(SMS), Multimedia Messaging Service (MMS), general packet radio service(GPRS), WAP, ultra-wide band (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), Session InitiationProtocol/Real-time Transport Protocol (SIP/RTP), or any of a variety ofother wired and wireless communication protocols. Network interface 332is sometimes known as a transceiver, transceiving device, or networkinterface card (NIC). Network computer 300 may optionally communicatewith a base station (not shown), or directly with another computer.

Audio interface 356 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 356 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgment forsome action. A microphone in audio interface 356 can also be used forinput to or control of network computer 300, for example, using voicerecognition.

Display 350 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. In some embodiments, display 350 may be a handheldprojector or pico projector capable of projecting an image on a wall orother object.

Network computer 300 may also comprise input/output interface 338 forcommunicating with external devices or computers not shown in FIG. 3.Input/output interface 338 can utilize one or more wired or wirelesscommunication technologies, such as USB™, Firewire™, WiFi, WiMax,Thunderbolt™, Infrared, Bluetooth™, Zigbee™, serial port, parallel port,and the like. Also, input/output interface 338 may also include one ormore sensors for determining geolocation information (e.g., GPS),monitoring electrical power conditions (e.g., voltage sensors, currentsensors, frequency sensors, and so on), monitoring weather (e.g.,thermostats, barometers, anemometers, humidity detectors, precipitationscales, or the like), or the like. Sensors may be one or more hardwaresensors that collect or measure data that is external to networkcomputer 300. Human interface components can be physically separate fromnetwork computer 300, allowing for remote input or output to networkcomputer 300. For example, information routed as described here throughhuman interface components such as display 350 or keyboard 352 caninstead be routed through the network interface 332 to appropriate humaninterface components located elsewhere on the network. Human interfacecomponents include any component that allows the computer to take inputfrom, or send output to, a human user of a computer. Accordingly,pointing devices such as mice, styluses, track balls, or the like, maycommunicate through pointing device interface 358 to receive user input.

GPS transceiver 340 can determine the physical coordinates of networkcomputer 300 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 340 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of network computer 300 on thesurface of the Earth. It is understood that under different conditions,GPS transceiver 340 can determine a physical location for networkcomputer 300. In one or more embodiments, however, network computer 300may, through other components, provide other information that may beemployed to determine a physical location of the client computer,including for example, a Media Access Control (MAC) address, IP address,and the like.

In at least one of the various embodiments, applications, such as,operating system 306, event analysis engine 322, other applications 329,or the like, may be arranged to employ geo-location information toselect one or more localization features, such as, time zones,languages, currencies, currency formatting, calendar formatting, or thelike. Localization features may be used in user interfaces, dashboards,visualizations, reports, as well as internal processes or databases. Inat least one of the various embodiments, geo-location information usedfor selecting localization information may be provided by GPS 340. Also,in some embodiments, geolocation information may include informationprovided using one or more geolocation protocols over the networks, suchas, wireless network 108 or network 111.

Memory 304 may include Random Access Memory (RAM), Read-Only Memory(ROM), or other types of memory. Memory 304 illustrates an example ofcomputer-readable storage media (devices) for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Memory 304 stores a basic input/output system (BIOS) 308for controlling low-level operation of network computer 300. The memoryalso stores an operating system 306 for controlling the operation ofnetwork computer 300. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX, orLINUX™, or a specialized operating system such as MicrosoftCorporation's Windows® operating system, or the Apple Corporation's OSX®operating system. The operating system may include, or interface withone or more virtual machine modules, such as, a Java virtual machinemodule that enables control of hardware components or operating systemoperations via Java application programs. Likewise, other runtimeenvironments may be included.

Memory 304 may further include one or more data storage 310, which canbe utilized by network computer 300 to store, among other things,applications 320 or other data. For example, data storage 310 may alsobe employed to store information that describes various capabilities ofnetwork computer 300. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 310 may also be employed to store socialnetworking information including address books, buddy lists, aliases,user profile information, or the like. Data storage 310 may furtherinclude program code, data, algorithms, and the like, for use by aprocessor, such as processor 302 to execute and perform actions such asthose actions described below. In one embodiment, at least some of datastorage 310 might also be stored on another component of networkcomputer 300, including, but not limited to, non-transitory media insideprocessor-readable removable storage device 336, processor-readablestationary storage device 334, or any other computer-readable storagedevice within network computer 300, or even external to network computer300. Data storage 310 may include, for example, event sources 314, statestreams 316, or the like.

Applications 320 may include computer executable instructions which,when executed by network computer 300, transmit, receive, or otherwiseprocess messages (e.g., SMS, Multimedia Messaging Service (MMS), InstantMessage (IM), email, or other messages), audio, video, and enabletelecommunication with another user of another mobile computer. Otherexamples of application programs include calendars, search programs,email client applications, IM applications, SMS applications, Voice OverInternet Protocol (VOIP) applications, contact managers, task managers,transcoders, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 320 may include event analysis engine 322, otherapplications 329, or the like, that may be arranged to perform actionsfor embodiments described below. In one or more of the variousembodiments, one or more of the applications may be implemented asmodules or components of another application. Further, in one or more ofthe various embodiments, applications may be implemented as operatingsystem extensions, modules, plugins, or the like.

Furthermore, in one or more of the various embodiments, analysis engine322, other applications 329, or the like, may be operative in acloud-based computing environment. In one or more of the variousembodiments, these applications, and others, that comprise themanagement platform may be executing within virtual machines or virtualservers that may be managed in a cloud-based based computingenvironment. In one or more of the various embodiments, in this contextthe applications may flow from one physical network computer within thecloud-based environment to another depending on performance and scalingconsiderations automatically managed by the cloud computing environment.Likewise, in one or more of the various embodiments, virtual machines orvirtual servers dedicated to event analysis engine 322, otherapplications 329, or the like, may be provisioned and de-commissionedautomatically.

Also, in one or more of the various embodiments, event analysis engine322, other applications 329, or the like, may be located in virtualservers running in a cloud-based computing environment rather than beingtied to one or more specific physical network computers.

Further, network computer 300 may also comprise hardware security module(HSM) 360 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information such as,keys, digital certificates, passwords, passphrases, two-factorauthentication information, or the like. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI), and may be employed to generate, manage, orstore keys pairs, or the like. In some embodiments, HSM 360 may be astand-alone network computer, in other cases, HSM 360 may be arranged asa hardware card that may be installed in a network computer.

Additionally, in one or more embodiments (not shown in the figures),network computer 300 may include an embedded logic hardware deviceinstead of a CPU, such as, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Array (FPGA), Programmable Array Logic(PAL), or the like, or combination thereof. The embedded logic hardwaredevice may directly execute its embedded logic to perform actions. Also,in one or more embodiments (not shown in the figures), the networkcomputer may include one or more hardware microcontrollers instead of aCPU. In one or more embodiments, the one or more microcontrollers maydirectly execute their own embedded logic to perform actions and accesstheir own internal memory and their own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions, such as System On a Chip (SOC), or the like.

Illustrative Logical System Architecture

FIG. 4 illustrates a logical architecture of system 400 for eventsequence analysis in accordance with one or more of the variousembodiments. In one or more of the various embodiments, system 400 maybe an event analysis platform arranged to include various componentsincluding: event analysis engine 402; one or more event sources, suchas, event source 404; one or more event data stores, such as, as eventstore 406, one or more state stream stores, such as, state streams 408;or the like. In one or more of the various embodiments, event analysisengines may be arranged to receive one or more queries or generate oneor more query results, such as, queries/results 410, or the like.

In one or more of the various embodiments, event source 404 representsapplications, services, or the like, that produce event information. Insome embodiments, event information may be a collection of eventsgenerated by one or more application or services. Also, in one or moreof the various embodiments, event sources may be applications, services,or systems that produce log files, or the like.

In one or more of the various embodiments, events may be associated withstate transitions for various systems. In some embodiments, anindividual event may correspond to a state transition. Also, in someembodiments, an individual state transition may be associated with twoor more events. Likewise, in some embodiments, one or more differentevents may be associated with the same state transition. In one or moreof the various embodiments, events or event information may be stored indata stores, such as, databases, file systems, or the like.

In one or more of the various embodiments, events may be ingested orstored by event store 406 in real-time or near real-time. Likewise, insome embodiments, events may be generated or collected prior to makingthem available to event analysis engine 402.

In one or more of the various embodiments, events may be associated withone or more entities, one or more states or state transitions, timevalues or timestamps, or the like.

In one or more of the various embodiments, event analysis engine 402 maybe arranged to process event information from event store 406 togenerate state streams that are associated with entities based on thestate transitions included in the events. In one or more of the variousembodiments, state streams may be generated in advance and stored in adata store, such as, state streams 408. Also, in some embodiments,analysis engines may be arranged to generate state streams on-the-fly.

Accordingly, in one or more of the various embodiments, clientcomputers, other applications, services, or the like, may providequeries to event analysis engine 402 represented here by queries/results410. In some embodiments, query information may include pattern filtersthat event analysis engines may employ to generate query results.

In one or more of the various embodiments, event analysis engines may bearranged to employ pattern filters to determine one or more entitiesbased on a match of the pattern filter with the state streams.Accordingly, in some embodiments, if a pattern filter matches a statestream, the one or more entities associated with that state stream maybe determined from the matched state stream data structures.

In one or more of the various embodiments, a query may be arranged togenerate results that may be based on the state streams that may bematched. For example, a query that is seeking a count of state streamsthat match pattern filter may be answered directly from the count ofmatched state streams. Alternatively, in some embodiments, patternfilters may be employed to determine a set of entities before applyingother query actions to those entities.

FIG. 5 illustrates a logical schematic of data structures 500 for eventsequence analysis in accordance with one or more of the variousembodiments. In one or more of the various embodiments, events 502represents a collection of events as they may be presented or stored inan event store. In this example, each record in events 502 represents anevent. Accordingly, in this example, the columns of events 502 representat least a portion of the fields or attributes of an event. In thisexample, column 504 represents a time or timestamp field that stores atime value associated with each event. In some embodiments, column 506represents an entity field that may be associated with each event, inthis example, column 506 represents a customer identifier that may beassociated with each event. And, in this example, column 508 represent astate field that stores state or state transition information that maybe associated with each event.

In one or more of the various embodiments, keyed events 510 represents adata structure for storing events that are keyed to a state key. In someembodiments, state keys may be distinct values that correspond to astate or state type. Accordingly, in some embodiments, each state orstate transition (hereinafter referred to as states) may be associatedwith a key value that may be unique with respect to other states in thesame system or scope. In this example, there are five states, including:Check-in, Evaluate, Repair, Test-Drive, and Deliver. Accordingly, inthis example, the state/key pairings may be (Check-in, a), (Evaluate,b), (Repair, c), (Test-Drive, d), and (Deliver, e).

In one or more of the various embodiments, event analysis engines may bearranged to use a variety of different formats or values for state keysas long as they may be distinct for the set of the states that are ofinterest. In some embodiments, state keys may be characters or stringsthat may be evaluated using regular expressions, or the like.

In one or more of the various embodiments, event analysis engines may bearranged to generate state streams for each entity by combining orconcatenating the state keys associated with a given entity. In thisexample, state streams 516 represents a collection of state streams thatare based on events 502 or keyed events 510. In this example, eachrecord in state streams 516 includes at least an entity identifier and astate stream comprised of the concatenated state keys for an entity. Insome embodiments, each state stream may be arranged such that the statekeys are ordered based on the order of their occurrence relative to eachentity.

In this example, for some embodiments, entities, such as, Jones, Smith,and Brown, may be considered to represent customers of an automobileservice organization. In this example, customers may drop off theirvehicles for repair and retrieve them if they are ready for delivery.Accordingly, in this example, a state stream that represents states inthe sequence: Check-In, Evaluate, Repair, Test-Drive, and Deliver may beconsidered to represent an ideal workflow. In contrast, for example, astate stream that includes this sequence: Check-In, Evaluate, Repair,Test-Drive, Evaluate, Repair, Test-Drive, Deliver may be considered lessthan ideal because the first repair was not sufficient to pass atest-drive.

Accordingly, in this example, event analysis engines may be arranged toidentify cases where the workflow for repairing a vehicle is less thanideal. For example, if an organization wants to compare thecosts/revenue for the different customers, a first comparison may showcustomer Smith (e.g., record 524) or customer Brown (e.g., record 526)may have higher cost or revenue than customer Jones (e.g., record 522).However, in this example, customer Jones' repair workflow went throughless states than customer Brown and also in this example, customerSmith's vehicle is not ready for delivery. So comparing cost/revenue, orthe like, of these three customers with each other may not providemeaningful comparisons. However, if an event analysis engine employed apattern filter to identify all the customers that have reached theDelivery state, subsequent analysis that compared costs/revenue may beconsidered more relevant. Likewise, comparing costs/revenue of customersthat went through the ideal workflow (e.g., customers like Jones) mayenable the organization to determine average costs/revenues forcustomers that are handled most efficiently. Accordingly, in someembodiments, event analysis engines that employ state streams mayimprove analysis results by efficiently incorporating state sequences toinclude or exclude entities from subsequent analysis.

FIG. 6 illustrates a logical representation of a portion of userinterface 600 for event sequence analysis in accordance with one or moreof the various embodiments. In some embodiments, user interface 500 maybe arranged to include one or more panels, such as, query panel 602,display panel 608, or the like.

In one or more of the various embodiments, user interface 600 may bedisplayed on one or more hardware displays, such as, client computerdisplays, mobile device displays, or the like. In some embodiments, userinterface 600 may be provided via a native application or as a webapplication hosted in a web browser or other similar applications. Oneof ordinary skill in the art will appreciate that for at least clarityor brevity many details common to commercial/production user interfaceshave been omitted from user interface 600. Likewise, in someembodiments, user interfaces may be arranged differently than showndepending on local circumstances or local requirements. However, one ofordinary skill in the art will appreciate that thedisclosure/description of user interface 600 is at least sufficient fordisclosing the innovations included herein.

In this example, query panel 602 is employed to enable users to enterquery information. In some embodiments, field 604 may be arranged toenable users to provide queries directed towards events. Also, in someembodiments, field 606 may be arranged to enable users to providepattern filters. In some embodiments, queries or pattern filters may becombined into a single expression or single field. Likewise, in someembodiments, additional fields may be provided to enable users to selectevent sources, format results, or the like.

In one or more of the various embodiments, pattern filters may be rulesor instructions that may be arranged to match sequences of states instate streams. In some embodiments, pattern filters may be defined usingrules or instructions that enable sequences of state keys to be matched.For example, in one or more of the various embodiments, pattern filtersmay be comprised of regular expressions arranged to match sequences ofstates that match particular patterns. For example, a per15 compatibleregular expression such as /.?abc./ may be employed to match each statestream that includes sequence of states that include abc. Likewise, forexample, a regular expression such as /{circumflex over ( )}a.c/ maymatch each state stream the begins with state a followed by any statefollowed by state c such as abc, axc, acc, or the like. One of ordinaryskill in the art will appreciate that there may be a variety of regularexpression languages using different notation. Accordingly, in one ormore of the various embodiments, event analysis engines may be arrangedto employ various regular expression engines or libraries. In someembodiments, event analysis engines may be arranged to determine whichregular expression dialects may be supported based on rules,instructions, configuration settings, or the like, provided viaconfiguration information to account for local requirements or localconventions. Further, in some embodiments, user interface 600 may bearranged to employ user interface elements that hide the underlyingmatching rules (regular expressions, or otherwise) from users. Forexample, user interfaces may be arranged to provide picker or selectioncontrols that enable users to define state sequence patterns withoutdirectly using regular expressions or other sequence pattern matchinginstructions.

In this example, display panel 608 is employed to display one or moreportions of a visualization of the results of queries or pattern filtersgenerated from events or state streams

In this example, display panel 608 includes visualization 610 that showquery results. In this example, visualization 610 represents a histogrambased on the number of state streams that matched a query or patternfilter. In this example, 3531 state streams include just the threestates, 896 state streams start with the three states with two statesfollowing the first three, and so on. In practice the particular resultsmay depend the state streams and the pattern filter.

Further, in one or more of the various embodiments, query informationmay include formatting or layout information. Also, in some embodiments,query information may be arranged such that pattern filters may beapplied before or after a query defined using a general purpose querylanguage be applied. For example, rather than generating a histogram ofstate streams that may match a sequence, a query result may be a lineplot of values that include or exclude items included in the line plotbased on whether their associated state streams were matched by apattern filter. For example, query results may be a visualization of therevenue generated by customers that are associated with a particularsequence of states. Thus, in some embodiments, conventional queries maybe applied to a set of data that has been selected of filtered based ontheir state streams and a pattern filter.

Generalized Operations

FIGS. 7-9 represent generalized operations for event sequence analysisin accordance with one or more of the various embodiments. In one ormore of the various embodiments, processes 700, 800, and 900 describedin conjunction with FIGS. 7-9 may be implemented by or executed by oneor more processors on a single network computer, such as networkcomputer 300 of FIG. 3. In other embodiments, these processes, orportions thereof, may be implemented by or executed on a plurality ofnetwork computers, such as network computer 300 of FIG. 3. In yet otherembodiments, these processes, or portions thereof, may be implemented byor executed on one or more virtualized computers, such as, those in acloud-based environment. However, embodiments are not so limited andvarious combinations of network computers, client computers, or the likemay be utilized. Further, in one or more of the various embodiments, theprocesses described in conjunction with FIGS. 7-9 may be used for eventsequence analysis in accordance with at least one of the variousembodiments or architectures such as those described in conjunction withFIGS. 4-6. Further, in one or more of the various embodiments, some orall of the actions performed by processes 700, 800, and 900 may beexecuted in part by event analysis engine 322 running on one or moreprocessors of one or more network computers.

FIG. 7 illustrates an overview flowchart of process 700 for eventsequence analysis in accordance with one or more of the variousembodiments. After a start block, at start block 702, in one or more ofthe various embodiments, event analysis engines may be arranged toprovide one or more events from an event data store. As described above,event data stores may be file systems, databases, or the like, whereevent information may be stored. In some embodiments, event informationmay include explicit events that may be generated by a dedicated eventsystem, such as, an anomaly detection system, network monitoring system,or the like. Also, in some embodiments, events may be informationentries that record workflow steps, operation actions, user activity, orthe like, rather than being limited to entries that may be designatedexpressly as events.

In one or more of the various embodiments, event information may includeinformation that the event analysis engine may interpret as discreteactions, some of which may include state information or state transitioninformation.

At block 704, in one or more of the various embodiments, event analysisengines may be arranged to determine entity fields, state fields, timefields, or the like, from the events. In some embodiments, eventinformation may include one or more fields or attributes. In someembodiments, one or more fields may correspond to an entity that anevent may primarily be associated with. In some embodiments, eventinformation may be associated with two or more entities.

Likewise, in one or more of the various embodiments, the eventinformation may include one or more field or attributes that maycorrespond to states or state transitions that may be associated withthe event.

Accordingly, in one or more of the various embodiments, event analysisengines may be arranged to determine the one or more fields in the eventinformation that may be associated with or may identify the entities ofinterest. In some embodiments, if two or more entities are included inan event records, event analysis engines may be arranged to identifysome or all of the entities.

Similarly, in one or more of the various embodiments, event analysisengines may be arranged to determine the fields or attributes in theevent information that may correspond to states or state transitions.

Further, in some embodiments, some event information may be associatedwith timestamps, or the like. Accordingly, in some embodiments, eventanalysis engines may be arranged to determine the time fields from theevent information.

In one or more of the various embodiments, event analysis engines may bearranged to employ various parsers, grammar, rules, or the like, todetermine the entities, states, or the like, that may be included in theevent information. Accordingly, in one or more of the variousembodiments, event analysis engines may be arranged to employ parsers,grammar, rules, or the like, provided via configuration information todetermine the entities, states, or time information from the eventinformation.

At block 706, in one or more of the various embodiments, event analysisengines may be arranged to generate state keys that correspond to eachstate transition. As described above, each unique state may be mapped toa state key. See also, FIG. 8 and its description for more detail.

At block 708, in one or more of the various embodiments, event analysisengines may be arranged to generate state streams for each entity. Asdescribed above, state streams may be generated by determining thestates for an entity from the event information and storing them in asequence that matches the order they occurred in the event information.For example, the first state in a state stream for an entity may beassociated with the first state transition that occurred for a givenentity.

At block 710, in one or more of the various embodiments, event analysisengines may be arranged to process query information and generate eventanalysis reports. In one or more of the various embodiments, eventanalysis engines may be arranged to provide user interfaces that enableusers to provide query information that may include pattern filtersdirected towards the state streams. In some embodiments, event analysisengines may be arranged to employ the pattern filters to determine thestate streams that may match the provided pattern filters. In someembodiments, state streams that match a pattern filter may be includedin a result set. In some embodiments, query information may include aquery that may be directed to the entities or events that may correspondto the state streams that matched the pattern filters.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 8 illustrates a flowchart of process 800 for event sequenceanalysis in accordance with one or more of the various embodiments.After a start block, at start block 802, in one or more of the variousembodiments, event analysis engines may be arranged to determine thestates or state transitions that may be included in one or more events.In one or more of the various embodiments, event sources may be files,streams, databases, or the like. Accordingly, in some embodiments,events may be provided in a variety of formats. In one or more of thevarious embodiments, in some formats (e.g., databases) the states orstate transition may be associated with a particular field or columnthat enables them to be easily or correctly identified. For example, ifevents are provided by a database table that includes a column thatincludes the state value, event analysis engines may determine thestates with a query to that database.

In one or more of the various embodiments, schemas, type definitions(e.g., DTDs for XML, documents, or the like), type maps, indexes,catalogs, data dictionaries, or the like, may be provided for a givenevent source. Further, in some embodiments, event analysis engines maybe arranged to provide user interfaces that enable users to identify thecriteria for identifying the states.

At block 804, in one or more of the various embodiments, event analysisengines may be arranged to map each state to a state key. As describedabove, in some embodiments, each state type may be mapped to a statekey. In some embodiments, the state keys may be generated such that theyare unique within the scope or context of a given event collection.Also, in some embodiments, state keys may be generated such that theyconform to the pattern matching engines that execute the patternfilters. Accordingly, in some embodiments, event analysis engines may bearranged to employ rules, grammars, parsers, maps, or the like, providedvia configuration information to determine the particular actions thatmay be executed to map states to state keys.

At block 806, in one or more of the various embodiments, event analysisengines may be arranged to generate state streams for each entitydiscovered in the events. As described above, state streams are based onthe sequence of states or state transitions that may be associated withan entity. In the example, described for FIG. 5 and FIG. 6, the entitieswould be the Customers. Accordingly, in those examples, a state streammay be generated for each Customer. In other embodiments, entities mayinclude, computers, applications, web sites, web pages, patients,vehicles, or the like. Generally, in some embodiments, entities may beanything that is going through the state transitions.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

FIG. 9 illustrates a flowchart of process 900 for event sequenceanalysis in accordance with one or more of the various embodiments.After a start block, at start block 902, in one or more of the variousembodiments, event analysis engines may be arranged to provide queryinformation. As described above, in some embodiments, users or otherservices may be enabled to provide query information that may beemployed for event analysis. In some embodiments, query information maybe provided via a user interface or configuration information. In someembodiments, event analysis engines may be arranged to employ variousrules, grammars, parsers, or the like, provided via configurationinformation to collect or interpret the query information.

At block 904, in one or more of the various embodiments, event analysisengines may be arranged to determine a query and a pattern filter fromthe query information. In one or more of the various embodiments,pattern filters may be provided separately from other query informationor it may be provided together with a query. In this context, patternfilters include instructions or rules for matching state streams whilethe query may be considered to include other query information. In someembodiments, the query may be one or more expressions that may becompatible with a data source or event stores that define additionalquery criteria besides the pattern filters. For example, in someembodiments, the query may be SQL-like instructions, or the like.

At block 906, in one or more of the various embodiments, event analysisengines may be arranged to determine one or more state streams thatmatch the pattern filter. In one or more of the various embodiments, theevent analysis engines may be arranged to apply the pattern filters tothe state streams to determine if there may be one or more matchingstate sequences in the state streams. In one or more of the variousembodiments, event analysis engines may be arranged to support more thanone pattern matching scheme. In some embodiments, pattern filters may becomprised of regular expressions compatible with one or more regularexpression engines or libraries, such as, perl compatible regularexpressions (PCRE), Javascript compatible regular expressions, pythoncompatible regular expressions, custom regular expressions, or the like.Accordingly, in some embodiments, event analysis engines may be arrangedto determine (or be provided) which regular expression to use based onconfiguration information.

In one or more of the various embodiments, event analysis engines may bearranged to employ other pattern matching engines that may not beregular expression engines. In some embodiments, pattern matchingengines (including regular expression engines) may be provided asplug-ins or dynamically linked shared libraries. Further, in someembodiments, event analysis engines may be arranged to employ patternmatching engines that are provided by third parties or external services(including remote micro-services).

In one or more of the various embodiments, state keys or state streamsmay be modified in advance or on-the-fly to conform to requirements ofthe pattern matching engine being employed.

In one or more of the various embodiments, event analysis engines may bearranged to generate a list of state streams that matched the patternfilter. In some embodiments, the generated list may include references(e.g., identifiers) to the entities, state streams, or the like, ratherthan copies of the state streams, or the like.

At decision block 908, in one or more of the various embodiments, if oneor more matching state streams may be determined, control may flow toblock 910; otherwise, control may flow block 912. In some embodiments,event analysis engines may be arranged to employ pattern filters toidentify state streams that include particular state sequences. In somecases, there may not be matching state streams. If there may not bematching sequences in the state streams, the result set may be empty.And, the query may be considered resolved.

At block 910, in one or more of the various embodiments, event analysisengines may be arranged to determine one or more entities or eventsbased on the match state streams. In one or more of the variousembodiments, each matching state stream may be associated with an entityor one or more events. Accordingly, in some embodiments, these entitiesor events may be determined from the state streams that matched thepattern filter.

At block 912, in one or more of the various embodiments, event analysisengines may be arranged to execute the query against the determinedentities or the determined events. In one or more of the variousembodiments, entities or events associated with the matching statestreams may be associated with a variety of other fields or attributes.Accordingly, in some embodiments, the query may include expressions thatare directed to these other fields or attributes. In some embodiments,the query may be configured to provide a query result directly from thestate streams, such as, a count of matching state streams, or the like.In such cases, the other entity attributes or fields may not beconsidered.

Next, in one or more of the various embodiments, control may be returnedto a calling process.

It will be understood that each block in each flowchart illustration,and combinations of blocks in each flowchart illustration, can beimplemented by computer program instructions. These program instructionsmay be provided to a processor to produce a machine, such that theinstructions, which execute on the processor, create means forimplementing the actions specified in each flowchart block or blocks.The computer program instructions may be executed by a processor tocause a series of operational steps to be performed by the processor toproduce a computer-implemented process such that the instructions, whichexecute on the processor, provide steps for implementing the actionsspecified in each flowchart block or blocks. The computer programinstructions may also cause at least some of the operational steps shownin the blocks of each flowchart to be performed in parallel. Moreover,some of the steps may also be performed across more than one processor,such as might arise in a multi-processor computer system. In addition,one or more blocks or combinations of blocks in each flowchartillustration may also be performed concurrently with other blocks orcombinations of blocks, or even in a different sequence than illustratedwithout departing from the scope or spirit of the invention.

Accordingly, each block in each flowchart illustration supportscombinations of means for performing the specified actions, combinationsof steps for performing the specified actions and program instructionmeans for performing the specified actions. It will also be understoodthat each block in each flowchart illustration, and combinations ofblocks in each flowchart illustration, can be implemented by specialpurpose hardware-based systems, which perform the specified actions orsteps, or combinations of special purpose hardware and computerinstructions. The foregoing example should not be construed as limitingor exhaustive, but rather, an illustrative use case to show animplementation of at least one of the various embodiments of theinvention.

Further, in one or more embodiments (not shown in the figures), thelogic in the illustrative flowcharts may be executed using an embeddedlogic hardware device instead of a CPU, such as, an Application SpecificIntegrated Circuit (ASIC), Field Programmable Gate Array (FPGA),Programmable Array Logic (PAL), or the like, or combination thereof. Theembedded logic hardware device may directly execute its embedded logicto perform actions. In one or more embodiments, a microcontroller may bearranged to directly execute its own embedded logic to perform actionsand access its own internal memory and its own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions, such as System On a Chip (SOC), or the like.

What is claimed as new and desired to be protected by Letters Patent ofthe United States is:
 1. A method for managing event information over anetwork using one or more network computers that include one or moreprocessors that perform actions, comprising: providing a plurality ofevents that are associated with one or more entities; determining aplurality of state types based on the plurality of events, wherein eachstate type is associated with a state key, and wherein each state key isdistinct from other state keys; determining one or more state keys thatare associated with each entity based on one or more events associatedwith each entity and the one or more state types; providing a statestream for each entity based on their one or more state keys, whereineach state stream is an ordered sequence of the one or more state keysthat are associated with each entity; and in response to a query thatincludes a pattern filter, performing further actions, including:employing the pattern filter to determine a portion of the one or moreentities based on the state stream for each entity, wherein the patternfilter matches the state stream for each of the portion of the entities;and generating a query result based on the query and the portion of theone or more entities.
 2. The method of claim 1, further comprising:concatenating the one or more state keys that are associated with eachentity into one or more sequences of states.
 3. The method of claim 1,wherein the pattern filter, further comprises, a regular expression thatis arranged to match one or more sequences of state keys.
 4. The methodof claim 1, wherein determining the state types, further comprises:determining one or more fields in the plurality of events that areassociated with one or more state transitions associated with the one ormore entities that are associated with the plurality of events.
 5. Themethod of claim 1, wherein determining a portion of the one or moreentities based on the state stream for each entity, further comprises:comparing each sequence of state keys to the pattern filter; determiningone or more state streams based on an affirmative result of thecomparison; and employing the one or more determined state streams todetermine each entity that corresponds to the one or more determinedstate streams.
 6. A processor readable non-transitory storage media thatincludes instructions for managing event information, wherein executionof the instructions by one or more processors, performs actions,comprising: providing a plurality of events that are associated with oneor more entities; determining a plurality of state types based on theplurality of events, wherein each state type is associated with a statekey, and wherein each state key is distinct from other state keys;determining one or more state keys that are associated with each entitybased on one or more events associated with each entity and the one ormore state types; providing a state stream for each entity based ontheir one or more state keys, wherein each state stream is an orderedsequence of the one or more state keys that are associated with eachentity; and in response to a query that includes a pattern filter,performing further actions, including: employing the pattern filter todetermine a portion of the one or more entities based on the statestream for each entity, wherein the pattern filter matches the statestream for each of the portion of the entities; and generating a queryresult based on the query and the portion of the one or more entities.7. The media of claim 6, further comprising: concatenating the one ormore state keys that are associated with each entity into one or moresequences of states.
 8. The media of claim 6, wherein the patternfilter, further comprises, a regular expression that is arranged tomatch one or more sequences of state keys.
 9. The media of claim 6,wherein determining the state types, further comprises: determining oneor more fields in the plurality of events that are associated with oneor more state transitions associated with the one or more entities thatare associated with the plurality of events.
 10. The media of claim 6,wherein determining a portion of the one or more entities based on thestate stream for each entity, further comprises: comparing each sequenceof state keys to the pattern filter; determining one or more statestreams based on an affirmative result of the comparison; and employingthe one or more determined state streams to determine each entity thatcorresponds to the one or more determined state streams.
 11. A systemfor managing event information, comprising: a network computer,comprising: a transceiver that communicates over the network; a memorythat stores at least instructions; and one or more processors thatexecute instructions that perform actions, including: providing aplurality of events that are associated with one or more entities;determining a plurality of state types based on the plurality of events,wherein each state type is associated with a state key, and wherein eachstate key is distinct from other state keys; determining one or morestate keys that are associated with each entity based on one or moreevents associated with each entity and the one or more state types;providing a state stream for each entity based on their one or morestate keys, wherein each state stream is an ordered sequence of the oneor more state keys that are associated with each entity; and in responseto a query that includes a pattern filter, performing further actions,including: employing the pattern filter to determine a portion of theone or more entities based on the state stream for each entity, whereinthe pattern filter matches the state stream for each of the portion ofthe entities; and generating a query result based on the query and theportion of the one or more entities; and a client computer, comprising:a transceiver that communicates over the network; a memory that storesat least instructions; and one or more processors that executeinstructions that perform actions, including: displaying the queryresult on a hardware display.
 12. The system of claim 11, wherein theone or more processors of the network computer execute instructions thatperform actions, further comprising: concatenating the one or more statekeys that are associated with each entity into one or more sequences ofstates.
 13. The system of claim 11, wherein the pattern filter, furthercomprises, a regular expression that is arranged to match one or moresequences of state keys.
 14. The system of claim 11, wherein determiningthe state types, further comprises: determining one or more fields inthe plurality of events that are associated with one or more statetransitions associated with the one or more entities that are associatedwith the plurality of events.
 15. The system of claim 11, whereindetermining a portion of the one or more entities based on the statestream for each entity, further comprises: comparing each sequence ofstate keys to the pattern filter; determining one or more state streamsbased on an affirmative result of the comparison; and employing the oneor more determined state streams to determine each entity thatcorresponds to the one or more determined state streams.
 16. A networkcomputer for managing event information, comprising: a transceiver thatcommunicates over the network; a memory that stores at leastinstructions; and one or more processors that execute instructions thatperform actions, including: providing a plurality of events that areassociated with one or more entities; determining a plurality of statetypes based on the plurality of events, wherein each state type isassociated with a state key, and wherein each state key is distinct fromother state keys; determining one or more state keys that are associatedwith each entity based on one or more events associated with each entityand the one or more state types; providing a state stream for eachentity based on their one or more state keys, wherein each state streamis an ordered sequence of the one or more state keys that are associatedwith each entity; and in response to a query that includes a patternfilter, performing further actions, including: employing the patternfilter to determine a portion of the one or more entities based on thestate stream for each entity, wherein the pattern filter matches thestate stream for each of the portion of the entities; and generating aquery result based on the query and the portion of the one or moreentities.
 17. The network computer of claim 16, wherein the one or moreprocessors execute instructions that perform actions, furthercomprising: concatenating the one or more state keys that are associatedwith each entity into one or more sequences of states.
 18. The networkcomputer of claim 16, wherein the pattern filter, further comprises, aregular expression that is arranged to match one or more sequences ofstate keys.
 19. The network computer of claim 16, wherein determiningthe state types, further comprises: determining one or more fields inthe plurality of events that are associated with one or more statetransitions associated with the one or more entities that are associatedwith the plurality of events.
 20. The network computer of claim 16,wherein determining a portion of the one or more entities based on thestate stream for each entity, further comprises: comparing each sequenceof state keys to the pattern filter; determining one or more statestreams based on an affirmative result of the comparison; and employingthe one or more determined state streams to determine each entity thatcorresponds to the one or more determined state streams.